bucket,
		)

		for k, v := range usageInfo.ObjectSizesHistogram {
			ch <- prometheus.MustNewConstMetric(
				prometheus.NewDesc(
					prometheus.BuildFQName(bucketNamespace, "objects", "histogram"),
					"Total number of objects of different sizes in a bucket",
					[]string{"bucket", "object_size"}, nil),
				prometheus.GaugeValue,
				float64(v),
				bucket,
				k,
			)
		}

	}

	return sm, nil
}

func getLatency(promAPI promv1.API, workloadName, workloadNamespace string, duration time.Duration, quantile float64) (time.Duration, error) {
	latencyQuery := fmt.Sprintf(`histogram_quantile(%f, sum(rate(%s_bucket{%s=~"%s.*", %s=~"%s.*",reporter="destination"}[%s])) by (le))`,
		quantile, reqDur, destWorkloadLabel, workloadName, destWorkloadNamespaceLabel, workloadNamespace, duration)

                     ->points.at(0)
                     ->histogram_value.sum());

  TFE_MonitoringSamplerCellAdd(cell, 5.0);
  metrics = collection_registry->CollectMetrics(options);
  EXPECT_EQ(6.0, metrics->point_set_map.at("test/sampler")
                     ->points.at(0)
                     ->histogram_value.sum());
  TFE_MonitoringDeleteBuckets(buckets);
  TFE_MonitoringDeleteSampler0(sampler);

          "targets": [
            {
              "datasource": {
                "type": "prometheus",
                "uid": "${datasource}"
              },
              "expr": "histogram_quantile(0.5, sum(rate(pilot_proxy_convergence_time_bucket[1m])) by (le))",
              "format": "time_series",
              "intervalFactor": 1,
              "legendFormat": "p50 ",
              "refId": "A"

import org.opensearch.search.aggregations.bucket.filter.FilterAggregationBuilder;
import org.opensearch.search.aggregations.bucket.global.GlobalAggregationBuilder;
import org.opensearch.search.aggregations.bucket.histogram.DateHistogramAggregationBuilder;
import org.opensearch.search.aggregations.bucket.missing.MissingAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.DateRangeAggregationBuilder;

- A new histogram metric to track the time it took to delete a job by the `TTLAfterFinished` controller ([#98676](https://github.com/kubernetes/kubernetes/pull/98676), [@ahg-g](https://github.com/ahg-g))

    If an SLO/alert has been defined based on `volume_operation_total_seconds`, it might get violated because of the more accurate metric might be significantly larger than previously reported. The metric is defined to be a histogram and the new semantic could change the distribution. ([#78061](https://github.com/kubernetes/kubernetes/pull/78061), [@yuxiangqian](https://github.com/yuxiangqian))

- `transformation_latencies_microseconds` -> `transformation_latencies_seconds`
- `data_key_generation_latencies_microseconds` -> `data_key_generation_latencies_seconds`

## Notable Features

- Increased the histogram resolution of the API server client certificate  to accommodate short-lived (< 6h) client certificates. ([#74806](https://github.com/kubernetes/kubernetes/pull/74806), [@mxinden](https://github.com/mxinden))

`SparseTensor.shape` has been renamed to `SparseTensor.dense_shape`. Same for
`SparseTensorValue.shape`. * Replace tf.scalar_summary, tf.histogram_summary,
tf.audio_summary, tf.image_summary with tf.summary.scalar, tf.summary.histogram,
tf.summary.audio, tf.summary.image, respectively. The new summary ops take name
rather than tag as their first argument, meaning summary ops now respect

- The `priority_level_request_utilization` metric histogram is adjusted so that for the cases where `phase=waiting` the denominator is the cumulative capacity of all of the priority level's queues.